@misc{10481/68871,
year = {2021},
url = {http://hdl.handle.net/10481/68871},
abstract = {Motivated by mapping adverse artifactual events caused by body movements in electroencephalographic (EEG) signals, we present a functional independent component analysis based on
the spectral decomposition of the kurtosis operator of a smoothed principal component expansion.
A discrete roughness penalty is introduced in the orthonormality constraint of the covariance eigenfunctions in order to obtain the smoothed basis for the proposed independent component model.
To select the tuning parameters, a cross-validation method that incorporates shrinkage is used to
enhance the performance on functional representations with a large basis dimension. This method
provides an estimation strategy to determine the penalty parameter and the optimal number of
components. Our independent component approach is applied to real EEG data to estimate genuine
brain potentials from a contaminated signal. As a result, it is possible to control high-frequency
remnants of neural origin overlapping artifactual sources to optimize their removal from the signal.
An R package implementing our methods is available at CRAN.},
organization = {Methusalem funding from the Flemish Government},
organization = {Project MTM2017-88708-P of the Spanish Ministry of Science, Innovation and Universities},
organization = {Project FQM-307 of the Government of Andalusia (Spain)},
publisher = {MDPI},
keywords = {Functional data},
keywords = {Functional kurtosis},
keywords = {Penalized splines},
keywords = {Smoothed principal components},
keywords = {Auditory–motor coupling task},
keywords = {EEG},
keywords = {Motion artifacts},
title = {Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal},
doi = {10.3390/math9111243},
author = {Vidal, Marc and Rosso, Mattia and Aguilera Del Pino, Ana María},
}